Bookmark

Computer Science > Multiagent Systems

Title:Fair Task Allocation in Crowdsourced Delivery

Abstract: Faster and more cost-efficient, crowdsourced delivery is needed to meet the
growing customer demands of many industries, including online shopping,
on-demand local delivery, and on-demand transportation. The power of
crowdsourced delivery stems from the large number of workers potentially
available to provide services and reduce costs. It has been shown in social
psychology literature that fairness is key to ensuring high worker
participation. However, existing assignment solutions fall short on modeling
the dynamic fairness metric. In this work, we introduce a new assignment
strategy for crowdsourced delivery tasks. This strategy takes fairness towards
workers into consideration, while maximizing the task allocation ratio. Since
redundant assignments are not possible in delivery tasks, we first introduce a
2-phase allocation model that increases the reliability of a worker to complete
a given task. To realize the effectiveness of our model in practice, we present
both offline and online versions of our proposed algorithm called F-Aware.
Given a task-to-worker bipartite graph, F-Aware assigns each task to a worker
that minimizes unfairness, while allocating tasks to use worker capacities as
much as possible. We present an evaluation of our algorithms with respect to
running time, task allocation ratio (TAR), as well as unfairness and assignment
ratio. Experiments show that F-Aware runs around 10^7 x faster than the
TAR-optimal solution and allocates 96.9% of the tasks that can be allocated by
it. Moreover, it is shown that, F-Aware is able to provide a much fair
distribution of tasks to workers than the best competitor algorithm.